diff --git a/torchao/quantization/linear_quant_modules.py b/torchao/quantization/linear_quant_modules.py
index de6755a55d..98cf68a8d2 100644
--- a/torchao/quantization/linear_quant_modules.py
+++ b/torchao/quantization/linear_quant_modules.py
@@ -86,10 +86,8 @@ def __init__(
         self,
         in_features: int,
         out_features: int,
-        # TODO: remove dtype field, not used
         bias=False,
         device=None,
-        dtype=None,
         groupsize: int = 128,
         inner_k_tiles: int = 8,
         precision: torch.dtype = torch.bfloat16,
@@ -110,9 +108,6 @@ def __init__(
         self.precision = precision
         self.scales_precision = scales_precision
 
-        if dtype is not None:
-            raise ValueError("Please specify 'precision' instead of 'dtype'")
-
         assert out_features % 8 == 0, "require out_features % 8 == 0"
         assert in_features % (inner_k_tiles * 16) == 0, (
             "require in_features % (innerKTiles * 16) == 0"
@@ -125,7 +120,7 @@ def __init__(
                         out_features,
                         in_features // 2,
                     ),
-                    dtype=torch.uint8,
+                    precision=torch.uint8,
                     device=device,
                 ),
             )
@@ -139,16 +134,16 @@ def __init__(
                         32,
                         inner_k_tiles // 2,
                     ),
-                    dtype=torch.int32,
+                    precision=torch.int32,
                     device=device,
                 ),
             )
-        self.dtype = dtype
+        self.precision = precision
         self.register_buffer(
             "scales_and_zeros",
             torch.zeros(
                 (in_features // groupsize, out_features, 2),
-                dtype=self.scales_precision,
+                precision=self.scales_precision,
                 device=device,
             ),
         )
@@ -410,8 +405,6 @@ def __init__(
         out_features: int,
         bias=True,
         device=None,
-        # TODO: remove this field, not used
-        dtype=None,
         groupsize: int = 256,
         precision: torch.dtype = torch.float32,
         scales_precision: torch.dtype = torch.float32,
@@ -434,26 +427,23 @@ def __init__(
         # that his module represents.
         self.precision = precision
 
-        if dtype is not None:
-            raise ValueError("Please specify 'precision' instead of 'dtype'")
-
         # currently storing unpacked int8 weights
         self.register_buffer(
             "weight",
-            torch.zeros((out_features, in_features), dtype=torch.int8),
+            torch.zeros((out_features, in_features), precision=torch.int8),
         )
         self.register_buffer(
             "scales",
             torch.zeros(
                 (out_features, in_features // groupsize),
-                dtype=scales_precision,
+                precision=scales_precision,
             ),
         )
         self.register_buffer(
             "zeros",
             torch.zeros(
                 (out_features, in_features // groupsize),
-                dtype=scales_precision,
+                precision=scales_precision,
             ),
         )